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AI-driven tools tend to produce extreme responses. On one hand there is “It's magic!” And the crowd “The best thing ever!” Meanwhile, we find “We are destined!” camp. Of course, these are not static or monolithic groups. During the day, you may even find yourself on either side of the spectrum.
I think the best way to resist exaggeration is to see how LLMS (and the products they have made possible) works and how they don't. What they can achieve and where they continue to struggle.
For a subtle approach to how AI tools work inside, we recommend exploring this week's highlights. You can see that some myths have been arrested and more insights are available.
Generic AI Myths, Bust: An Engineer's Quick Guide
Faced with frequent questions (and increased fear) about the role and impact of AI, Amyma wanted to clarify what the fuss was for her engineering colleagues. The result was a clear, accessible, and level head introductory book to technologies that even skilled industry veterinarians can have difficulty understanding.
Deploy AI safely and responsibly
What do you need to build a reliable AI application? Stephanie Kirmer and some of her recent IEEE co-panelists have a sharp and practical look at some of the most enduring myths surrounding AI ethics and its daily challenges, from observationality to governance.
Rag Description: Understanding embedding, similarity, and search
The searched generation was with us for quite some time, but some of its components remained unquestioned. Maria Mouschoutzi's latest explanator is tackling some general knowledge gaps.
Most Read Stories of the Week
Career paths, data analytics, and education in AI: explore the stories that generated the biggest talk in the community last week.
How to Become a Machine Learning Engineer with Egor Howell (Step-by-Step)
My experiment with Notebooklm for education by Parul Pandey
From Python to Javascript: A playbook for data analysis on N8N with examples of code nodes by Samir Saci
Other recommended readings
From immersive, deep dives on universal calculations to a thorough guide to causal reasoning in retail analysis, don't miss out on the latest and outstanding articles.
- Analysis of retail sales shifts with causal impact: a case study at Carrefour by Thanh Liêm Nguyen
- Implementing a coffee machine project in Python using object-oriented programming with Mahnoor Javed
- Himalaya Bill Shrester explores Python's achievement order and marginal cost curve
- Rapid prototyping of chatbots using reimlit and chainlit by Chinmay kakatkar
Contribute to TDS
We love publishing articles from new authors, so if you recently wrote an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it?
